LSTM architecture was introduced in 1997, which is a type of RNN that uses special units in adding to standard units [1]}. LSTM units comprise a ‘memory cell’ that can preserve information in memory for long periods. A set of gates is used to regulate when information arrives in the memory of a cell. There are three different types of gates, i.e., input gate, output gate, and forget gate. The input gate selects how much information from the last example will be reserved in memory; the output gate controls the amount of data delivered to the next layer, and forget gates regulate the tearing rate of memory keeping. This planning lets them learn longer-term reliance [2]}. Fig. REF  show the architecture of LSTM. Fig. REF  shows the architecture of LSTM consisting of three gates i.e., forget gate, remember gate, and output gate, deciding about the data sequence. In LSTM, the tanh activation function controls the values that flow across the network whereas the sigmoid activation function \((\sigma )\)  is used at the gates. In LSTM, the tanh activation function controls the values that flow across the network whereas the sigmoid activation function \((\sigma )\)  is used at the gates as a gating function, it produces an output value of either 0 or 1 that allows either no flow or complete flow of information throughout the gates. In addition, X denotes the input that flows throughout the network to acquire output related to that input.
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